1. Field of the Invention
The present disclosure relates in general to the fields of driver assistance and autonomous driving. Specifically, a technique for recognizing traffic signs is indicated. The technique can be implemented as a method, as a device, or as a computer program.
2. Description of the Related Art
In Automatic traffic sign recognition is an essential feature of many vehicles. A wide variety of methods for recognizing traffic signs are conceivable in this connection.
In one method (“Traffic Sign Recognition”), the images of recorded traffic signs are allocated to a specific traffic sign class on the basis of specific characteristic features. A circular traffic sign, for example, can be allocated to a traffic sign that indicates a speed limit. On the basis of a feature which is class-specific to this specific traffic sign class, for example the number block in the case of speed limits, the recorded image can then be allocated to a specific traffic sign within this traffic sign class. The matching process outputs a result that indicates how reliably the recorded image indicates the specific traffic sign from the traffic sign class.
Visibility conditions, inter alia, are decisive for the reliability of a method of this type, which is based purely on the matching of class-specific features of image data with stored class-specific features. In poor visibility conditions, such as those caused by fog and rain, the risk exists of traffic signs within one class being confused with one another, for example the 30 km/h speed limit sign with the 130 km/h speed limit sign, or even the risk of traffic signs from different traffic sign classes being confused with one another, for example the 12t maximum weight limit sign with the 120 km/h speed limit sign. It is furthermore known for the rear lights of an automobile driving in front to be mistakenly recognized as the 60 km/h speed limit sign.
A different method (“Traffic Sign Fusion”) is based on a combination of camera data with map data containing traffic sign information. The traffic sign recorded and recognized by the camera is compared with the traffic signs from the map data expected at the present position. In the event of a complete match, the corresponding traffic sign is output to the driver. In the event of an incomplete match, a higher or lower evaluation of the camera data can be carried out depending on the recognition probability and, as a result, either the camera data or the map data can be prioritized, or both data can be rejected.
Plausibility checks can be completed here through the application of defined acceptance criteria. For example, a traffic sign with a speed limit of 80 km/h is improbable within a built-up area, so that, in this case, the map data are prioritized. The definition and development of these hard-coded acceptance criteria are costly and time-consuming. In order to improve these acceptance criteria, they must be geared towards specific driving circumstances, which entails a substantial testing requirement. If no camera data are available, traffic signs known from a map are used, which are output to the driver without further verification.